This increasing cyber threat on smart grid NCSs generates necessary to design advanced preventive measures that protect the integrity, confidentiality, and availability of the crucial infrastructure. This paper explains the designing of an intrusion detection framework with the support of a deep feed-forward neural network coupled with a lattice cryptosystem for the control infrastructures of smart grid. The lattice cryptosystem is used between the plant and the control center for encryption and decryption, preventing False Data Injection Attacks (FDIA) from being successful, as data exchange is secured. Meanwhile, at the control center, neural networks are used for the detection of Denial-of-Service attacks, which might occur, through the identification of malicious traffic patterns and ensuring availability of the system. The suggested methodology is applied in Simulink using the IEEE 39-bus system, which offers a real-world simulation environment to test its efficiency. It is achieved through analysis that post-quantum crypto-graphic security together with AI-powered anomaly detection considerably in-creases the cyber-attack resilience of smart grid infrastructures. The solution presented is a scalable, intelligent, and strong one for the protection of next-generation smart grids from modern cyber threats.
